Global internet of things (IoT) quality of service (QoS) realization through collaborative edge gateways

ABSTRACT

Global Internet of things (IoT) quality of service (QoS) is provided through self-forming, self-healing, and/or collaborative edge IoT gateways. Moreover, global IoT services are provided by logically extending cellular networks with roaming partners to backhaul, track, and/or manage the globally deployed edge IoT gateways. In one aspect, real-time QoS and/or monitoring capabilities for the global IoT services can be provided through a communication between the edge IoT gateways and an edge gateway controller deployed within a cloud. The edge IoT gateways form a structured mesh network to coordinate workload execution under control of the edge gateway controller, which can facilitate a highly efficient QoS and/or SLA management for mobile IoT sensors, to provide a secure monitoring and/or diagnostic capability for the global IoT services.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 16/208,227 (now U.S. Pat. No.11,108,849), filed Dec. 3, 2018, and entitled “GLOBAL INTERNET OF THINGS(IOT) QUALITY OF SERVICE (QOS) REALIZATION THROUGH COLLABORATIVE EDGEGATEWAYS,” the entirety of which application is hereby incorporated byreference herein.

TECHNICAL FIELD

The subject disclosure relates to wireless communications, e.g.,utilization of collaborative edge gateways for providing unified qualityof service (QoS) for global Internet of things (IoT).

BACKGROUND

Internet of things (IoT) technology holds a great promise for the futureof the global communications industry. As the number of connecteddevices that can establish connectivity with other devices and/orpassive objects to exchange data continues to rise steadily, the IoTtechnology gains widespread proliferation in the information technologyindustry. With an anticipated projection of over 20 billion devices inthe next few years, service providers, network providers and/or cloudproviders will observe a net increase in their traffic handlingcapabilities. This can help the providers enable new IoT servicestailored to targeted industry verticals. While there are several ongoingcompetitive developments in the IoT domain, some key areas where thereis an immediate focus include smart city, transportation and/or utilityservices, virtual and augmented reality, etc. Low power wide areanetworking technologies using third generation partnership project(3GPP) defined standards and their ongoing evolution towards fifthgeneration (5G) seem to provide a solid framework to support suchmassive IoT initiatives.

Conventional network providers offer IoT services nationally (e.g.,within the United States) and have complete control of the networkwithin the country to efficiently manage a service level agreement(SLA). However, offering IoT service internationally, can create newchallenges for network providers with regards to QoS/SLA management.

The above-described background relating to mobility networks is merelyintended to provide a contextual overview of some current issues and isnot intended to be exhaustive. Other contextual information may becomefurther apparent upon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system that provides a global Internet ofthings (IoT) service management platform that facilitates quality ofservice (QoS) realization through a network of collaborative edge IoTgateways.

FIG. 2 illustrates an example system that facilitates workloadmanagement across globally distributed edge IoT gateways to provideservices for mobile IoT devices.

FIG. 3 illustrates an example system that comprises an edge IoT gatewaynode utilized to facilitate a global IoT service.

FIG. 4 illustrates an example system for global QoS and/or service levelagreement (SLA) management for IoT services.

FIG. 5 illustrates an example system that comprises a self-forming,self-healing, and/or collaborative edge IoT gateway, according to anaspect of the subject disclosure.

FIG. 6 illustrates an example system that facilitates automating one ormore features in accordance with the subject embodiments.

FIG. 7 illustrates an example method that facilitates global QoSrealization through management of a network of collaborative edge IoTgateways.

FIG. 8 illustrates an example method for providing real-time QoS andmonitoring capabilities for global IoT services.

FIG. 9 illustrates a block diagram of a computer operable to execute thedisclosed communication architecture.

FIG. 10 illustrates a schematic block diagram of a computing environmentin accordance with the subject specification.

DETAILED DESCRIPTION

One or more embodiments are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various embodiments. It may be evident,however, that the various embodiments can be practiced without thesespecific details, e.g., without applying to any particular networkedenvironment or standard. In other instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing the embodiments in additional detail.

As used in this application, the terms “component,” “module,” “system,”“interface,” “node,” “platform,” “server,” “controller,” “entity,”“element,” “gateway,” or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution or an entity related to anoperational machine with one or more specific functionalities. Forexample, a component may be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, computer-executable instruction(s), a program, and/or acomputer. By way of illustration, both an application running on acontroller and the controller can be a component. One or more componentsmay reside within a process and/or thread of execution and a componentmay be localized on one computer and/or distributed between two or morecomputers. As another example, an interface can comprise input/output(I/O) components as well as associated processor, application, and/orAPI components.

Further, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement one or moreaspects of the disclosed subject matter. An article of manufacture canencompass a computer program accessible from any computer-readabledevice or computer-readable storage/communications media. For example,computer readable storage media can comprise but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical disks (e.g., compact disk (CD), digital versatile disk(DVD) . . . ), smart cards, and flash memory devices (e.g., card, stick,key drive . . . ). Of course, those skilled in the art will recognizemany modifications can be made to this configuration without departingfrom the scope or spirit of the various embodiments.

In addition, the word “example” or “exemplary” is used herein to meanserving as an example, instance, or illustration. As used in thisapplication, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Terms like “user equipment” or similar terminology, refer to a wired orwireless communication-capable device utilized by a subscriber or userof a wired or wireless communication service to receive or convey data,control, voice, video, sound, gaming, or substantially any data-streamor signaling-stream. Data and signaling streams can be packetized orframe-based flows. Further, the terms “user,” “subscriber,” “consumer,”“customer,” and the like are employed interchangeably throughout thesubject specification, unless context warrants particular distinction(s)among the terms. It should be noted that such terms can refer to humanentities or automated components supported through artificialintelligence (e.g., a capacity to make inference based on complexmathematical formalisms), which can provide simulated vision, soundrecognition and so forth.

Furthermore, it is noted that the term “cloud” as used herein can referto a set of servers, communicatively and/or operatively coupled to eachother, that host a set of applications utilized for servicing userrequests. In general, the cloud computing resources can communicate withuser devices via most any wired and/or wireless communication network toprovide access to services that are based in the cloud and not storedlocally (e.g., on the user device). A typical cloud computingenvironment can include multiple layers, aggregated together, thatinteract with each other to provide resources for end-users.

Aspects or features of the disclosed subject matter can be exploited insubstantially any wired or wireless communication technology; e.g.,universal mobile telecommunications system (UMTS), Wi-Fi, worldwideinteroperability for microwave access (WiMAX), general packet radioservice (GPRS), enhanced GPRS, third generation partnership project(3GPP) long term evolution (LTE), fifth generation (5G) or other nextgeneration networks, third generation partnership project 2 (3GPP2)ultra mobile broadband (UMB), high speed packet access (HSPA), Zigbee,or another IEEE 802.XX technology, low power wide area (LPWA) and/ornon-3GPP standard based solutions, such as, but not limited to, Ingenu,Sigfox, and/or LoRa, etc. Additionally, substantially all aspects of thedisclosed subject matter can be exploited in legacy (e.g., wireline)telecommunication technologies.

Internet of things (IoT), which is the future of internet connectivity,enables creation of an information rich ecosystem that can enrich modernconnected way of life and transform the way in which businesses as wellas consumers function today. Typically, IoT/machine-to-machine (M2M)devices can have different characteristics than regular/commercial userequipment (UEs) (e.g., non-IoT devices, such as, but not limited to,smart phones, tablet computers, personal computers, etc.). For example,the IoT/M2M devices collectively generate a much greater number ofsignaling connections in the mobile core network as compared to regularUEs. Further, in another example, the service/application provider oftenperforms simultaneous device triggering and monitoring for targeted IoTapplications and services. The systems and methods disclosed herein canprovide various enhancements to conventional entities to enable IoTsecurity, quality of service (QoS), and/or service level agreement (SLA)for international/global IoT services.

As a variety of IoT device categories emerge based on 3GPP standardsevolution supporting a multitude of services, there is an increasingdemand on the various network functions within the mobilityinfrastructure to be more intelligent, dynamic, adaptive, and flexiblewith their interworking to provide the best possible node levelfunctions and end-to-end service behaviors. The systems and methodsdisclosed herein can provide real-time QoS and/or monitoringcapabilities for IoT services through interactions between massivelydistributed IoT gateways deployed globally that are managed by an edgegateway controller deployed within a cloud. In an aspect, the edgegateway controller can facilitate efficient QoS and/or SLA managementfor mobile IoT sensors to provide a secure monitoring and/or diagnosticcapability for IoT services. Although, the systems and methods disclosedherein are described with respect to globally distributed edge IoTgateways, it is noted that the subject disclosure is not limited to edgeIoT gateways that are distributed across different counties and can bedistributed nationally as well.

Referring initially to FIG. 1, there illustrated is an example system100 that provides a global IoT service management platform thatfacilitates QoS realization through a network of collaborative edge IoTgateways, according to one or more aspects of the disclosed subjectmatter. Typically, IoT gateway nodes communicate with a variety ofsensors that interact with external applications through different radiotechnologies, such as, but not limited to, radio-frequencyidentification (RFID), Wi-Fi, 3G, 4G, LTE, 5G, Zigbee, Z-wave,Bluetooth, video streams, etc. The sensors' interaction with theirlistening IoT gateways plays a key role and an opportunity to provide asecure and unified SLA/QoS to the end users. As an example, system 100can be employed for various IoT applications, such as, but not limitedto, smart manufacturing and/or industrial automation, transportation(e.g., shipping, delivery, ridesharing, connected cars, etc.), energy,wearables (e.g., health/fitness trackers, smart jewelry, etc.),FirstNet, healthcare, etc.

According to an embodiment, system 100 deploys, within edge clouds, agroup of distributed edge IoT gateway nodes 102 at various geographicallocations nationally and/or internationally, to form a unifiedcommunication protocol that enables real-time monitoring of IoT servicesand support for IoT QoS and/or SLA that is desirable by end users. Theedge clouds comprise a networked group of devices that are deployedwithin the logical extremes of a network (e.g., within a radio accessnetwork) away from centralized points (e.g., core mobility network).Moreover, the devices (e.g., distributed edge IoT gateway nodes 102) ofthe edge clouds allow data produced by tracked IoT devices 106 to beprocessed closer to where it is created instead of sending it acrosslong routes to core network clouds.

Moreover, the distributed edge IoT gateway nodes 102 can interact withboth a software-defined networking (SDN)-like control plane and a meshformation of the distributed edge IoT gateway nodes 102, to collectivelyprovide opportunity for global workload management and interaction.Moreover, the distributed edge IoT gateway nodes 102 can communicatewith IoT clients 104, for example, moving IoT client devices installedin trucks, airplanes, containers, drones, and/or other shippingelements, mobile network operator (MNO)-certified IoT gateways that canbe self installed by customers (e.g., shipping industries), etc.

Typically, the IoT clients 104 can listen to and/or communicate with alarge assortment of IoT devices 106 (e.g., sensors, etc.). As anexample, the tracked IoT devices 106 can comprises, but are not limitedto, most any sensors, smart meters, smart home devices, smart citydevices, tracking devices, security systems, smart energy grid devices,agricultural devices, connected vehicle, at least partially automatedvehicle (e.g., drones), a wearable device (e.g., smart watch, connectedglasses, wrist monitor, etc.), smart microelectromechanical systems(MEMS)-based IoT devices etc. It is noted that the tracked IoT devices106 can be mobile, have limited mobility and/or be stationary.Typically, different categories of IoT devices vary widely in terms oftheir service requirements, data throughput, latency, access priorityand/or connectivity reliability. As an example, a first category of IoTdevices can be delay tolerant, whereas a second category of IoT devicescan be highly prone to latency errors. Accordingly, different policiesand/or preferences with respect to workloads can be defined (e.g., via amaster orchestration component 108) for different categories of the IoTdevices 106.

In an aspect, the master IoT orchestration component 108 can be utilizedto manage the distributed edge IoT gateway nodes 102 and coordinateworkloads across the distributed edge IoT gateway nodes 102. Forexample, the master IoT orchestration component 108 can configure,assign, update, and/or terminate thin workloads for specific gateways ofthe distributed edge IoT gateway nodes 102. The workloads can specifytasks and/or actions that are to be performed based on measurementsreceived from the tracked devices 106. For example, in a shippingapplication, the master IoT orchestration component 108 can instantiateone or more IoT thin workloads at gateways that are deployed at targetlocations along a path on which the tracked device is expected totravel. Moreover, the distributed edge gateway nodes 102 can run a largenumber (e.g., millions) of thin workloads that each represent one ormore prediction or expectation of a tracked device 106.

The master IoT orchestration component 108 can be responsible fordetermining SLA declaration parameter sets that define the expectedSLA/QoS associated with a tracked device 106. As an example, adeclaration parameter can provide expected and unexpected statesassociated with the service, QoS, and/or interaction methods. Further,the master IoT orchestration component 108 can receive workload statefeedback from each workload triggered by its associated sensor(s). Basedon an analysis of the state feedback (and/or one or more machinelearning techniques), the master IoT orchestration component 108 canterminate and/or reconfigure the current workload and/or activate one ormore new workloads at another IoT gateway (e.g., deployed at a nextexpected location for a shipment).

The distributed IoT gateway nodes 102 form a structured mesh networkglobally over the Internet that can communicate asynchronously and/orsynchronously. In one aspect, the distributed IoT gateway nodes 102 canimplement advanced collaborative and/or distributed technologies (e.g.,Blockchain) to track/monitor QoS and SLA globally. As an example, ametadata (e.g., token) that represents workload state (e.g., summary oftasks performed, verified parameters, estimated time of arrival at nextlocation, expected state at next location, etc.) can be transferred fromone IoT gateway node to another. Collectively these states can beanalyzed (e.g., by the master IoT orchestration component 108) todetermine expected and unexpected scenarios, and accordingly,appropriate QoS and SLA service monitoring can be provided. For example,when a customer is shipping a shipment that is tracked by sensors insidethe shipment from US city of Cupertino to city of Milan, Italy, the pathfor this shipment can be provided to the master IoT orchestrationcomponent 108. The master IoT orchestration component 108 can compilethe path into the expected passages through the edge IoT gateway nodes102 along the path and can schedule workloads into these nodes. Thedesired/expected scenarios are that the shipment states and sensorstates are reported as the shipment moves through the nodal IoT gatewaypassages. As an example, the undesirable/unexpected states are when anexpected sensor is not received in timely manner, is received at anotheredge IoT gateway node, incorrect/different states or incorrect/differentset of devices are reported, etc. The asynchronous programming of theedge IoT gateway nodes for expected work load provides an optimized QoSmonitoring service and unexpected results interaction among the edge IoTgateway nodes 102 and with the master IoT orchestration component 108provides monitoring capabilities of these devices.

Referring now to FIG. 2, there illustrated is an example system 200 thatfacilitates workload management across globally distributed edge IoTgateways to provide services for mobile IoT devices, in accordance withan aspect of the subject disclosure. It is noted that the master IoTorchestration component 108 can comprise functionality as more fullydescribed herein, for example, as described above with regard to system100. Further, the distributed edge IoT gateway nodes 102, as describedabove with regard to system 100, can comprise edge IoT gateway nodes 202₁-202 _(x) (wherein x can be most any integer).

According to an aspect, MNOs can employ system 200 to provide IoTservices by utilizing their edge gateway clouds (e.g., comprising edgeIoT gateway nodes 202 ₁-202 _(x)), wherein their mobile networks (e.g.,LTE, 5G, etc.) can logically be extended with roaming partners tobackhaul, track and manage IoT hotspots internationally. As an example,edge IoT gateway nodes 202 ₁-202 _(x) can be deployed at the deep edgesin roaming spaces and can proxy manage any sensor associated with an IoTservice associated with the MNO's customers. The edge IoT gateway nodes202 ₁-202 _(x) can be deployed nationally and/or globally to form astructured mesh of edge gateways that are highly connected (e.g., viaLTE, Internet, and/or other communication technology) and can performone or more IoT workloads assigned by an edge IoT controller 204.Moreover, the edge IoT gateway nodes 202 ₁-202 _(x) can collectivelyform a distributed system that is controlled via the edge IoT controller204 and/or MNO's control plane devices. In an aspect, the edge IoTcontroller 204 can spin up millions of thin workloads (e.g., lowoverhead processes) as the sensors are recognized and/or sensor data isreceived by one or more roaming operators (e.g., IoT clients 104).Software-defined flexibility and control of the edge IoT gateway nodes202 ₁-202 _(x) can be provided by employing open (e.g., non-proprietary,standardized, protocol-agnostic, etc.) IoT gateway application programinterfaces (APIs) and/or stack ecosystems. Further, in some embodiments,the edge IoT controller 204 can utilize machine learning (ML)-basedbased models for realization of additional QoS, SLA, and/or monitoringservices.

Referring back to FIG. 2, there illustrated is an example expected path206 associated with one or more tracked IoT devices. As an example, path206 can comprise a shipment route that is typically travelled by a truckdelivering a package to a destination. It is noted that the tracked IoTdevices can be mobile themselves and/or can be placed within/attached toa vehicle/container that is mobile. Based on the expected path 206, theedge IoT controller 204 can assign and/or coordinate workloads to IoTgateway nodes that are deployed at locations along the path, forexample, edge IoT gateway nodes 202 ₁-202 ₅. As an example, edge IoTgateway nodes 202 ₁-202 _(x) can be owned, leased, and/or managed by theMNO. As a mobile IoT device (e.g., a shipment) moves through variouslocations (e.g., from homes, to trucks, to airports, to storagecontainers, etc.), it can interact with edge IoT gateway nodes 202 ₁-202₅, which can then perform actions based on the workload(s) assigned tothem. For example, the edge IoT controller 204 can determine and/orpredict arrival time of the shipment at the edge IoT gateway nodes 202₁-202 ₅ and accordingly, coordinate the workloads locally and/orinternationally. Moreover, the edge IoT controller 204 can control theedge IoT gateway nodes 202 ₁-202 ₅ and facilitate management and/orscheduling workloads across the nodes as it expects the shipment to movethough the areas at which the gateways are deployed.

Further, in one aspect, edge IoT controller 204 can learn and build frominternal states and/or external states associated with the service. Asan example, internal states can comprise workload state informationreceived from the edge IoT gateway nodes 202 ₁-202 _(x), such as, butnot limited to, arrival and/or departure time, sensor measurements(e.g., temperature of a container, image and/or video if the container,parameters associated with the vehicle, etc.), package characteristics,etc. Moreover, if determined that the internal state has deviated from anormal range, the edge IoT controller 204 can update, terminate, and/orreassign workloads. The external states can be received from externaldevices (e.g., web servers, content servers, etc.) and can compriseinformation, such as, but not limited to, news, weather data, eventschedules, etc. For example, in response to determining that severeweather conditions are being experienced in an area through which theshipment is originally being routed, the edge IoT controller 204 canpredict whether the shipment would be rerouted to/through anotherlocation and accordingly, reassign the workload to another IoT gatewaynode (e.g., IoT gateway node 202 _(x)) to perform the defined action(s)(e.g., expect the shipment, take action/perform functions onarrival/departure, report sensor measurements, check for error, failure,and/or alert conditions, etc.). Accordingly, as path changes aredetected (e.g., shipment is received at an IoT gateway node that is notlocated on/near the original route, shipment is received earlier orlater than expected, etc.) and/or are predicted (e.g., via internalstate data, external state data, ML, etc.), the edge IoT controller 204can dynamically update workload configuration across the edge IoTgateway nodes 202 ₁-202 _(x).

According to an embodiment, the edge IoT gateway nodes 202 ₁-202 _(x)can communicate with each other via a structured mesh network tofacilitate tracking and/or security. Technologies, such as but notlimited to, Blockchain can be utilized to generate tokens that can betransferred between the edge IoT gateway nodes 202 ₁-202 _(x). As anexample, the token generated by an IoT gateway node can provide asummary of a shipment state comprising a number of packages within theshipment, type of packaging, contents of a package, actions performed atthe IoT gateway node, a result of the actions performed at the IoTgateway node, departure time from the IoT gateway node, estimatedarrival time of the shipment at the next IoT gateway node along thepath, route information, etc.

Referring now to FIG. 3, there illustrated is an example system 300 thatcomprises an edge IoT gateway node 202 utilized to facilitate a globalIoT service, in accordance with an aspect of the subject disclosure. Asan example, the edge IoT gateway node 202 can comprise a MNO-managededge gateway that can be deployed nationally and/or internationally. Itis noted that the edge IoT gateway node 202 can be substantially similarto edge IoT gateway nodes 202 ₁-202 _(x) and can comprise functionalityas more fully described herein, for example, as described above withregard to edge IoT gateway nodes 202 ₁-202 _(x). Further, IoT devices106 ₁-106 _(M) (e.g., wherein M is most any integer) can besubstantially similar to tracked IoT devices 106 and can comprisefunctionality as more fully described herein, for example, as describedabove with regard to tracked IoT devices 106.

Typically, the edge IoT gateway node 202 can comprise edge computingnode hardware 302 (e.g., compute and storage resources) and an operatingsystem (OS)/container component 304 that execute workloads viamicroservices 1-N 306. As an example, the microservices 1-N 306 can be asuite of independently-deployable, small, modular services, wherein eachservice runs a unique process and communicates with the other overstandard protocols with well-defined interfaces to serve a businessgoal. Moreover, the workloads can be assigned and/or initiated viainstructions received from the IoT cloud platform (e.g., via edge IoTcontroller 204). In one aspect, the workloads can be initiated onreceiving a broadcast message from IoT devices 106 ₁-106 _(M), as theyapproach the edge IoT gateway node 202. Moreover, the IoT devices 106₁-106 _(M) can be programmed to broadcast messages when they reachparticular locations and/or enter certain areas (e.g. location of theedge IoT gateway node 202). Additionally, or alternatively, the edge IoTcontroller 204 can instruct the edge IoT gateway node 202 to initiatethe workloads at an estimated time of arrival of the IoT devices 106₁-106 _(M). According to an aspect, the edge IoT controller 204 candefine one or more policies for the initiation of workloads based onvarious parameters, for example, IoT arrival time, IoT measurement data,current time, historical preferences, etc. Typically, the edge IoTgateway node 202 can initiate different workloads in response todetecting expected scenarios or detecting anomalies. For example, ifdetermined that the IoT device 106 ₁ arrives at the expected time, afirst set of actions can be performed; if determined that the IoT device106 ₁ arrives before the expected time, a second set of actions can beperformed; and if determined that the IoT device 106 ₁ arrives after theexpected time, a third set of actions can be performed.

It is noted that the actions are not limited to automated actions (e.g.,classifying the shipment as undamaged, changing a temperature of acontainer, verifying that packages are not misplaced, stolen, replaced,etc.) and can also comprise manual actions performed by authorizedpersonnel. For example, if sensor data received from IoT device 106 ₁ isdetermined to be in a high range and/or weather data along the routeindicates high temperatures (e.g., above a defined temperaturethreshold), a workload can be initiated that alerts (e.g., via sendingmessages to UEs) appropriate persons to add coolant/ice to the packagingof the shipment. Manually performed actions can then be entered into thesystem (e.g., via messages sent from a UE or input via a user interfaceof/coupled to the edge IoT gateway node 202) and can be utilized togenerate state data associated with one or more workloads. Moreover,state data can comprise, but is not limited to, a summary of actionsperformed at the edge IoT gateway node 202, anomalies detected, timingdata associated with arrival and/or departure of the IoT device,workload QoS and/or SLA atomic measurements with action selectionfunctions that represent the received sensory transaction, etc. Thestate data can be provided, via IoT cloud platform 308, as feedback tothe edge IoT controller 204, which can analyze the feedback tofacilitate global QoS and/or SLA management. Additionally, oroptionally, at least a portion of the state data can be provided toanother edge IoT gateway node(s) (e.g., the next edge IoT gateway nodeon the path travelled by the IoT device 106 ₁) via edge IoT gateway meshnetwork 310. According to an embodiment, the edge IoT gateway node 202can dynamically form logical connections to any other edge IoT gatewaynode(s) as IoT QoS/SLA measurements can be aggregated. Moreover, the IoTstates can also become available through open and/or collaborative APIsand/or offline through alternative channels. Further, in one aspect, theedge IoT gateway node 202 can utilize advanced collaborative and/ordistributed blockchain techniques to facilitate tracking QoS and SLAglobally, for example, by transferring tokens to other edge IoT gatewaynode(s) via edge IoT gateway mesh network 310 (and/or to the edge IoTcontroller 204 via IoT cloud platform 308).

Referring now to FIG. 4, there illustrated is an example system 400 forglobal QoS and/or SLA management for IoT services, according to anaspect of the subject disclosure. It is noted that the edge IoTcontroller 204 can comprise functionality as more fully describedherein, for example, as described above with regard to system 200. Inone embodiment, the edge IoT controller 204 can comprise a statusmonitoring component 402 that can receive internal and/or external statedata. In an example, the internal state data can be received from one ormore of the distributed IoT gateway nodes 102 and can compriseinformation such as, but not limited to, a report of tasks performed,received sensor measurements, error/failure conditions, alerts, and/orunexpected scenarios (e.g., shipment is delayed or received earlier thanexpected, shipment arrives at an IoT gateway node that is not expectingthe shipment, shipment size has changed, etc.). In another example, theexternal state data can be received from web servers, content servers,application server, customer devices, etc. and can comprise informationsuch as, but not limited to, schedule information, event data, news,weather data, traffic reports, customer preferences or instructions,etc.

According to an embodiment, a workload management component 404 can beutilized to analyze the status data to schedule QoS-aware workloads.Moreover, the workload management component 404 can schedule and/ormanage the QoS-aware workloads based on machine learning and/or big dataanalytics (e.g., image recognition, identity, validation,classification, etc.) For example, if an image (or other measurementdata) of a package received from two (or more) edge IoT gateway nodesindicates that a size, shape, color, etc. has changed, then a workloadcan be created to flag the package and notify appropriate personnel tocheck on the package. In another example, if a shipment is rerouted dueto traffic congestion or weather conditions, workloads can be assignedto and initiated (e.g., at expected time of arrival of the shipment) ata new edge IoT gateway node that is deployed at a location on the newroute.

In one aspect, a provisioning component 406 can receive customerpreferences and/or instructions via one or more customer devices and/orportals (not shown). For example, a path for routing tracked IoT devices(e.g., a shipment) can be provided via the provisioning component 406and stored within data store 408. According to an embodiment, theworkload management component 404 can compile the path into a group ofexpected passages through select edge IoT gateway nodes deployed alongthe path and schedule workload into these edge IoT gateway nodes. Afirst set of workloads can be performed in response to detectingdesired/expected scenarios associated with the reported shipment statesand/or sensor states and a second set of workloads can be performed inresponse to detecting undesired/unexpected scenarios associated with thereported shipment states and/or sensor states. The asynchronousprogramming of the edge IoT gateway nodes for the workloads can providea highly efficient and optimal QoS monitoring

It is noted that data store 408 can store information, such as, but notlimited to, monitored status data, operator preferences and/or policies,customer preferences and/or policies, etc. that can be analyzed tofacilitate workload management (e.g., by the workload managementcomponent 404). Although data store 408 is depicted to reside within theedge IoT controller 204, it is noted that the subject specification isnot that limited and the data store 408 can reside (e.g., completely orpartially) outside the edge IoT controller 204 and can be remotelycoupled to the edge IoT controller 204. It is noted that the data store408 can comprise volatile memory(s) or nonvolatile memory(s), or cancomprise both volatile and nonvolatile memory(s). Examples of suitabletypes of volatile and non-volatile memory are described below withreference to FIG. 9. The memory (e.g., data stores, databases) of thesubject systems and methods is intended to comprise, without beinglimited to, these and any other suitable types of memory.

FIG. 5 illustrates an example system 500 that comprises a self-forming,self-healing, and/or collaborative edge IoT gateway, according to anaspect of the subject disclosure. MNOs can introduce global IoT servicesutilizing their edge gateway clouds where their LTE networks canlogically be extended with roaming partners to backhaul, track, andmanage IoT hotspots (e.g., edge IoT gateway node 202) internationally.IoT SLA data paths and/or measurements can be further realized throughhigher abstraction of network key performance indicator (KPI) that canbe shared between the collaborative operators or edge IoT gateway nodes.It is noted that the edge IoT gateway node 202 can comprisefunctionality as more fully described herein, for example, as describedabove with regard to systems 200-300.

In one aspect, a communication component 502 can be utilized to interactwith IoT devices and/or clients to receive measurement data, forexample, when the IoT devices and/or clients arrive at and/or couple tothe edge IoT gateway node 202. Based on the received measurement data, aworkload execution component 504 can run one or more workloads assignedby an edge IoT controller. As an example, the workloads can classify thedata (e.g., classify the package as damaged or satisfactory),authenticate a device identifier (e.g., verify that the package has notbeen misplaced, lost, stolen. replaced, etc.), and/or enforce most anydefined policy(ies). For example, a policy, determined by the edge IoTcontroller, can specify a list of actions that are to be performed for aparticular shipment with a specified group identifier/device identifier.Actions can comprise, but are not limited to, recording sensormeasurements and/or a photo/video of a box, classifying that the box isnot damaged and/or verifying that the image matches reference image,scanning barcode/RFID code to ensure accuracy; etc. A feedback and alertreporting component 506 can be utilized to determine state dataassociated with the workload (e.g., summary of actions performed,anomalies detected, etc.) and report the state data to the edge IoTcontroller and optionally to other edge IoT gateway nodes. As anexample, the state data can become available through open and/orcollaborative APIs or offline through alternative channels.

Further, a tracking component 508 can be utilized to transfer statusdata and/or other information to edge IoT gateway nodes via a structuredmesh network (e.g., through LTE networks and/or Internet). Typically,the edge IoT gateway node 202 can dynamically form logical connectionsto any other nodes as aggregated IoT QoS/SLA measurements can beaggregated. In one embodiment, the tracking component 508 can utilizeBlockchain technology to create and transfer tokens and/or metadata toone or more other edge IoT gateway nodes to facilitate tracking and/orsecurity.

Referring now to FIG. 6, there illustrated is an example system 600 thatemploys an artificial intelligence (AI) component (602) to facilitateautomating one or more features in accordance with the subjectembodiments. It can be noted that the edge IoT controller 204, statusmonitoring component 402, workload management component 404,provisioning component 406, and data store 408 can comprisefunctionality as more fully described herein, for example, as describedabove with regard to systems 200-500.

In an example embodiment, system 600 (e.g., in connection withautomatically managing workloads) can employ various AI-based schemes(e.g., intelligent processing/analysis, machine learning, etc.) forcarrying out various aspects thereof. For example, a process forscheduling, updating, and/or terminating workloads can be facilitatedvia an automatic classifier system implemented by AI component 602.Moreover, the AI component 602 can exploit various artificialintelligence (AI) methods or machine learning methods. Artificialintelligence techniques can typically apply advanced mathematicalanalysis—e.g., decision trees, neural networks, regression analysis,principal component analysis (PCA) for feature and pattern extraction,cluster analysis, genetic algorithm, or reinforced learning—to a dataset. In particular, AI component 602 can employ one of numerousmethodologies for learning from data and then drawing inferences fromthe models so constructed. For example, hidden markov models (HMMs) andrelated prototypical dependency models can be employed. Generalprobabilistic graphical models, such as Dempster-Shafer networks andBayesian networks like those created by structure search using aBayesian model score or approximation can also be utilized. In addition,linear classifiers, such as support vector machines (SVMs), non-linearclassifiers like methods referred to as “neural network” methodologies,fuzzy logic methodologies can also be employed.

As will be readily appreciated from the subject specification, anexample embodiment can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing device/operator preferences, historical information,receiving extrinsic information, type of service, type of device, etc.).For example, SVMs can be configured via a learning or training phasewithin a classifier constructor and feature selection module. Thus, theclassifier(s) of AI component 602 can be used to automatically learn andperform a number of functions, comprising but not limited to determiningaccording to a predetermined criteria, when and which workloads are tobe initiated, IoT edge gateways nodes at which specific workloads are tobe instantiated, actions that are to be performed, updates that are tobe made to a workload, when a workload is to be terminated, etc. Thecriteria can comprise, but is not limited to, historical patterns and/ortrends, network operator preferences and/or policies, customerpreferences, predicted traffic flows, event data, latency data,reliability/availability data, current time/date, workload statefeedback data, weather data, type of IoT device, news, and the like.

The next generation of devices and their smart connectivity as well asmessage delivery in the mobility infrastructure places significantdemands on the networks to be intelligent, dynamic, flexible, proactive,and maintain closed-loop active communication. According to anembodiment, the network architecture disclosed herein provides severalnon-limiting advantages and features such as, but not limited to globalIoT QoS realization and improved network resiliency and/or robustnessthrough collaborative edge gateways.

FIGS. 7-8 illustrate flow diagrams and/or methods in accordance with thedisclosed subject matter. For simplicity of explanation, the flowdiagrams and/or methods are depicted and described as a series of acts.It is to be understood and noted that the various embodiments are notlimited by the acts illustrated and/or by the order of acts, for exampleacts can occur in various orders and/or concurrently, and with otheracts not presented and described herein. Furthermore, not allillustrated acts may be required to implement the flow diagrams and/ormethods in accordance with the disclosed subject matter. In addition,those skilled in the art will understand and note that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be further noted thatthe methods disclosed hereinafter and throughout this specification arecapable of being stored on an article of manufacture to facilitatetransporting and transferring such methods to computers. The termarticle of manufacture, as used herein, is intended to encompass acomputer program accessible from any computer-readable device orcomputer-readable storage/communications media.

Referring now to FIG. 7 there illustrated is an example method 700 thatfacilitates global QoS realization through management of a network ofcollaborative edge IoT gateways, according to an aspect of the subjectdisclosure. In an aspect, method 700 can be implemented by one or morecontrol plane devices (e.g., edge IoT controller 204) of a communicationnetwork (e.g., cellular network). At 702, customer provisioning data canbe received. For example, the customer provisioning data can comprise,but is not limited to, shipping routes, customer preferences and/orpolicies, IoT device information, etc. In one aspect, the provisioningdata can be utilized to identify edge IoT gateways that are locatedalong one or more shipping routes and accordingly, at 704, QoS-awareworkloads can be scheduled at the identified edge IoT gateways.

Further, at 706, internal and/or external state data can be determined.In an example, the internal state data can be received from one or moreof the distributed IoT gateway nodes and can comprise workload stateinformation such as, but not limited to, a report of tasks performed,received sensor measurements, error/failure conditions, alerts, and/orunexpected scenarios. In another example, the external state data can bereceived from web servers, content servers, application server, customerdevices, etc. and can comprise information such as, but not limited to,schedule information, event data, news, weather data, traffic reports,customer preferences or instructions, etc. At 708, the state data can beanalyzed (e.g., based on ML techniques) to facilitate a management ofthe QoS-aware workloads. For example, the QoS-aware workloads can beupdated and/or terminated, and/or new QoS-aware workloads can bescheduled at different (or the same) edge IoT gateways to provideoptimal QoS monitoring.

FIG. 8 illustrates an example method 800 for providing real-time QoS andmonitoring capabilities for global IoT services, according to an aspectof the subject disclosure. As an example, method 800 can be implementedby one or more edge network devices (e.g., edge IoT gateway node 202) ofa communication network (e.g., cellular network). At 802, workload datacan be received from an edge IoT controller. As an example, the workloaddata can comprise a set of actions that are to be performed via one ormore microservices executed by the edge IoT gateway node. Further, at804, the execution of the workload can be initiated in response toreceiving communication data (e.g., broadcast messages) from an IoTdevice associated with the workload data. For example, the workload canbe initiated in response to determining that the IoT device has entereda geographical area served by the edge IoT gateway node and/or iscoupled to the edge IoT gateway node.

Furthermore, at 806, feedback data can be determined and provided to theedge IoT controller at most any time, for example, prior to, subsequentto, and/or during the workload execution. As an example, the feedbackdata can comprise, but is not limited to, a summary/list/result ofactions performed via workload execution, measurements received from theIoT device, failure/error conditions, unexpected behavior and/orsituations, alerts, etc. Moreover, the edge IoT controller can utilizethe feedback data to manage (e.g., update, terminate, reassign, etc.)the workloads. In addition, at 808, tracking data can be determined andprovided to other edge IoT gateways via a mesh network, for example, tofacilitate tracking and/or security (e.g., by employing a Blockchaintechnology).

Referring now to FIG. 9, there is illustrated a block diagram of acomputer 902 operable to execute the disclosed communicationarchitecture. In order to provide additional context for various aspectsof the disclosed subject matter, FIG. 9 and the following discussion areintended to provide a brief, general description of a suitable computingenvironment 900 in which the various aspects of the specification can beimplemented. While the specification has been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that thespecification also can be implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will note thatthe various methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the specification can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disk (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or other tangible and/ornon-transitory media which can be used to store desired information.Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, radio frequency (RF),infrared and other wireless media.

With reference again to FIG. 9, the example environment 900 forimplementing various aspects of the specification comprises a computer902, the computer 902 comprising a processing unit 904, a system memory906 and a system bus 908. As an example, the component(s),application(s), client(s), server(s), equipment, system(s),interface(s), gateway(s), controller(s), node(s), cloud(s), entity(ies),function(s), platform(s), and/or device(s) (e.g., distributed edge IoTgateway nodes 102, IoT clients 104, tracked IoT devices 106, IoT devices106 ₁-106 _(M), master IoT orchestration component 108, edge IoT gatewaynodes 202 ₁-202 _(x), edge IoT controller 204, edge computing nodehardware 302, OS/container component 304, IoT cloud platform 308, statusmonitoring component 402, workload management component 404,provisioning component 406, data store 408, communication component 502,workload execution component 504, feedback and alert reporting component506, tracking component 508, AI component 602, etc.) disclosed hereinwith respect to systems 100-600 can each comprise at least a portion ofthe computer 902. The system bus 908 couples system componentscomprising, but not limited to, the system memory 906 to the processingunit 904. The processing unit 904 can be any of various commerciallyavailable processors. Dual microprocessors and other multi-processorarchitectures can also be employed as the processing unit 904.

The system bus 908 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 906comprises read-only memory (ROM) 910 and random access memory (RAM) 912.A basic input/output system (BIOS) is stored in a non-volatile memory910 such as ROM, EPROM, EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer902, such as during startup. The RAM 912 can also comprise a high-speedRAM such as static RAM for caching data.

The computer 902 further comprises an internal hard disk drive (HDD)914, which internal hard disk drive 914 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 916, (e.g., to read from or write to a removable diskette918) and an optical disk drive 920, (e.g., reading a CD-ROM disk 922 or,to read from or write to other high capacity optical media such as theDVD). The hard disk drive 914, magnetic disk drive 916 and optical diskdrive 920 can be connected to the system bus 908 by a hard disk driveinterface 924, a magnetic disk drive interface 926 and an optical driveinterface 928, respectively. The interface 924 for external driveimplementations comprises at least one or both of universal serial bus(USB) and IEEE 1394 interface technologies. Other external driveconnection technologies are within contemplation of the subjectdisclosure.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 902, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a HDD, a removable magnetic diskette, and a removable optical mediasuch as a CD or DVD, it should be noted by those skilled in the art thatother types of storage media which are readable by a computer, such aszip drives, magnetic cassettes, flash memory cards, solid-state disks(SSD), cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methods ofthe specification.

A number of program modules can be stored in the drives and RAM 912,comprising an operating system 930, one or more application programs932, other program modules 934 and program data 936. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 912. It is noted that the specification can beimplemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 902 throughone or more wired/wireless input devices, e.g., a keyboard 938 and/or apointing device, such as a mouse 940 or a touchscreen or touchpad (notillustrated). These and other input devices are often connected to theprocessing unit 904 through an input device interface 942 that iscoupled to the system bus 908, but can be connected by other interfaces,such as a parallel port, an IEEE 1394 serial port, a game port, a USBport, an IR interface, etc. A monitor 944 or other type of displaydevice is also connected to the system bus 908 via an interface, such asa video adapter 946.

The computer 902 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 948. The remotecomputer(s) 948 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer902, although, for purposes of brevity, only a memory/storage device 950is illustrated. The logical connections depicted comprise wired/wirelessconnectivity to a local area network (LAN) 952 and/or larger networks,e.g., a wide area network (WAN) 954. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which canconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 902 is connectedto the local network 952 through a wired and/or wireless communicationnetwork interface or adapter 956. The adapter 956 can facilitate wiredor wireless communication to the LAN 952, which can also comprise awireless access point disposed thereon for communicating with thewireless adapter 956.

When used in a WAN networking environment, the computer 902 can comprisea modem 958, or is connected to a communications server on the WAN 954,or has other means for establishing communications over the WAN 954,such as by way of the Internet. The modem 958, which can be internal orexternal and a wired or wireless device, is connected to the system bus908 via the serial port interface 942. In a networked environment,program modules depicted relative to the computer 902, or portionsthereof, can be stored in the remote memory/storage device 950. It willbe noted that the network connections shown are example and other meansof establishing a communications link between the computers can be used.

The computer 902 is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., desktopand/or portable computer, server, communications satellite, etc. Thiscomprises at least Wi-Fi and Bluetooth™ wireless technologies or othercommunication technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity networks use radio technologies called IEEE802.11 (a, b, g, n, etc.) to provide secure, reliable, fast wirelessconnectivity. A Wi-Fi network can be used to connect computers to eachother, to the Internet, and to wired networks (which use IEEE 802.3 orEthernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radiobands, at an 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, forexample, or with products that contain both bands (dual band), so thenetworks can provide real-world performance similar to the basic 10BaseTwired Ethernet networks used in many offices.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Processors can exploit nano-scale architectures suchas, but not limited to, molecular and quantum-dot based transistors,switches and gates, in order to optimize space usage or enhanceperformance of user equipment. A processor may also be implemented as acombination of computing processing units.

In the subject specification, terms such as “data store,” data storage,”“database,” “cache,” and substantially any other information storagecomponent relevant to operation and functionality of a component, referto “memory components,” or entities embodied in a “memory” or componentscomprising the memory. It will be noted that the memory components, orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can comprise read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), or flash memory. Volatile memory can comprise random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such assynchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchlinkDRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, thedisclosed memory components of systems or methods herein are intended tocomprise, without being limited to comprising, these and any othersuitable types of memory.

Referring now to FIG. 10, there is illustrated a schematic block diagramof a computing environment 1000 in accordance with the subjectspecification. The system 1000 comprises one or more client(s) 1002. Theclient(s) 1002 can be hardware and/or software (e.g., threads,processes, computing devices).

The system 1000 also comprises one or more server(s) 1004. The server(s)1004 can also be hardware and/or software (e.g., threads, processes,computing devices). The servers 1004 can house threads to performtransformations by employing the specification, for example. Onepossible communication between a client 1002 and a server 1004 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The data packet may comprise a cookie and/orassociated contextual information, for example. The system 1000comprises a communication framework 1006 (e.g., a global communicationnetwork such as the Internet, cellular network, etc.) that can beemployed to facilitate communications between the client(s) 1002 and theserver(s) 1004.

Communications can be facilitated via a wired (comprising optical fiber)and/or wireless technology. The client(s) 1002 are operatively connectedto one or more client data store(s) 1008 that can be employed to storeinformation local to the client(s) 1002 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1004 areoperatively connected to one or more server data store(s) 1010 that canbe employed to store information local to the servers 1004.

What has been described above comprises examples of the presentspecification. It is, of course, not possible to describe everyconceivable combination of components or methods for purposes ofdescribing the present specification, but one of ordinary skill in theart may recognize that many further combinations and permutations of thepresent specification are possible. Accordingly, the presentspecification is intended to embrace all such alterations, modificationsand variations that fall within the spirit and scope of the appendedclaims. Furthermore, to the extent that the term “comprises” is used ineither the detailed description or the claims, such term is intended tobe inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: based onprovisioning data associated with a shipment that is associated with adevice, assigning a workload associated with the shipment to a firstedge gateway device of edge gateway devices that are deployed in ageographic area; in response to receiving state data associated with theworkload, and based on an analysis of the state data that indicates adefined condition is associated with a first route that the shipment isinitially scheduled to travel towards the first edge gateway device,predicting, with a threshold probability, that the shipment will bererouted from the first route to a second route associated with a secondedge gateway device of the edge gateway devices due in part to thedefined condition; and in response to determining that the state datasatisfies an unexpected state criterion based on the predicting,modifying the workload by terminating assignment of the workload to thefirst edge gateway device and reassigning the workload to the secondedge gateway device.
 2. The system of claim 1, wherein the provisioningdata comprises route data indicative of a route via which the device ispredicted to travel, wherein the route comprises the first route, andwherein the operations further comprise selecting the first edge gatewaydevice from the edge gateway devices based on the route data.
 3. Thesystem of claim 1, wherein the state data comprises external state datarelating to the defined condition, comprising a weather condition or atraffic condition, associated with the shipment initially being routedtowards the first edge gateway device, wherein the predicting comprises:based on the analysis of the external state data, predicting, with thethreshold probability, the shipment will be rerouted from the firstroute associated with the first edge gateway device to the second routeassociated with the second gateway device due in part to the weathercondition or the traffic condition, and wherein the modifying comprisesthe terminating of the assignment of the workload to the first edgegateway device and the reassigning of the workload to the second edgegateway device, based on the predicting, with the threshold probability,that the shipment will be rerouted from the first route to the secondroute.
 4. The system of claim 1, wherein the operations furthercomprise: receiving the state data, comprising internal state data orexternal state data, from the device, an edge gateway device, a networkdevice, a content server, a web server, or a sensor, wherein the edgegateway device is the first edge gateway device, the second edge gatewaydevice, or a third edge gateway device of the edge gateway devices. 5.The system of claim 1, wherein the operations further comprise receivingthe state data via an open or collaborative application programminginterface.
 6. The system of claim 1, wherein the edge gateway devicesare arranged to form a structured mesh network of edge gateway devicesto facilitate transfer of shipment-related data associated withshipments between the edge gateway devices of the structured meshnetwork of edge gateway devices, wherein the shipment-related data isusable to monitor a quality of service associated with a service that isassociated with the edge gateway devices, and wherein theshipment-related data comprises tracking data, route data, informationrelating to a package that is part of the shipment, action informationregarding an action performed by the first edge gateway device or thesecond edge gateway device, time information relating to a time of anevent associated with the shipment, or a portion of the state datarelating to the shipment.
 7. The system of claim 6, wherein theoperations further comprise: generating a token or metadata relating tothe shipment based on the shipment-related data and based on ablockchain-based technology; and transferring the token between thefirst edge gateway device and the second edge gateway device tofacilitate tracking or security of the shipment.
 8. The system of claim1, wherein the operations further comprise determining whether to modifythe workload based on a service level agreement associated with a globalInternet of things service relating to transportation of shipmentscomprising the shipment.
 9. The system of claim 1, wherein theoperations further comprise executing the workload by the second edgegateway device based on measurement data relating to the shipment thatis received from the device.
 10. The system of claim 1, wherein theoperations further comprise: instructing the second edge gateway deviceto perform a first action relating to processing the shipment, inresponse to the device being determined to arrive at a particularlocation at an expected time; instructing the second edge gateway deviceto perform a second action relating to the processing of the shipment,in response to the device being determined to arrive at the particularlocation prior to the expected time; or instructing the second edgegateway device to perform a third action relating to the processing ofthe shipment, in response to the device being determined to arrive atthe particular location after the expected time.
 11. The system of claim1, wherein the edge gateway devices comprise a third edge gatewaydevice, and wherein the assigning comprises assigning the workloadassociated with the shipment to the first edge gateway device over thethird edge gateway device based on the provisioning data and based onthe first edge gateway device being determined to be located closer tothe device associated with the shipment than the third edge gatewaydevice.
 12. A method, comprising: based on provisioning informationassociated with a package that is associated with a device, scheduling,by a system comprising a processor, an assignment of a workloadassociated with the package to a first edge gateway device of edgegateway devices that are deployed in a geographic region; in response toreceiving state information associated with the workload, and based onan analysis of the state information indicating a defined condition isassociated with a first route on which the package is initiallyscheduled to be transported towards the first edge gateway device,predicting, by the system and with a threshold likelihood, that thepackage will be rerouted from the first route to a second routeassociated with a second edge gateway device of the edge gateway devicesas a result of the defined condition; and in response to determiningthat the state information satisfies an unexpected state criterion basedon the predicting, updating, by the system, the scheduling to reassignthe workload from the first edge gateway device to the second edgegateway device.
 13. The method of claim 12, further comprising:receiving, by the system, the provisioning information associated with aservice that utilizes devices, comprising the device, to facilitateshipment of packages comprising the package; based on the provisioninginformation, determining, by the system, route information indicative ofa route via which the device is expected to travel, wherein the routecomprises the first route; and selecting, by the system, the first edgegateway device of the edge gateway devices based on the first route. 14.The method of claim 12, further comprising: based on the provisioninginformation, determining, by the system, parameter information thatdefines an expected quality of service associated with the device,wherein the scheduling comprises: based on the provisioning informationand the parameter information, scheduling the assignment of the workloadassociated with the package to the first edge gateway device.
 15. Themethod of claim 12, further comprising receiving, by the system, thestate information from the device, an edge gateway device, a networkdevice, a content server, a web server, or a sensor, wherein the edgegateway device is the first edge gateway device, the second edge gatewaydevice, or a third edge gateway device of the edge gateway devices. 16.The method of claim 12, wherein the scheduling, the predicting, or theupdating is performed based on a first result of machine learningperformed on package-related information relating to the package or asecond result of big data analytics performed on the package-relatedinformation, and wherein the package-related information comprises theprovisioning information, the state information, or informationregarding the package.
 17. The method of claim 12, wherein theassignment of the workload is a first assignment of a first workload,and wherein the method further comprises: performing, by the system, amachine learning analysis on images of the package received from two ormore of the edge gateway devices, wherein the machine learning analysisis performed using a machine learning technology; determining, by thesystem, whether a characteristic of the package has changed based on aresult of the machine learning analysis performed on the images of thepackage; and in response to determining the characteristic of thepackage has changed, scheduling, by the system, a second assignment of asecond workload to the second edge gateway device or a third edgegateway device of the edge gateway devices to facilitate checking astatus or an integrity of the package.
 18. The method of claim 12,further comprising determining, by the system, whether to update thescheduling to reassign the workload from the first edge gateway deviceto the second edge gateway device based on a service level agreementassociated with a global Internet of things service relating totransportation of the package.
 19. A non-transitory machine-readablemedium, comprising executable instructions that, when executed by aprocessor, facilitate performance of operations, comprising: based onprovisioning data associated with a package that is associated with adevice, determining an assignment of a workload associated with thepackage to a first edge gateway device of edge gateway devices that aredeployed in a geographic area; in response to receiving state dataassociated with the workload, and based on an analysis of the state dataindicating a condition is associated with a first path on which thepackage is initially scheduled to be transported towards the first edgegateway device, determining that the package will be rerouted from thefirst path to a second path associated with a second edge gateway deviceof the edge gateway devices due at least in part to the condition; andin response to determining that the state data satisfies an unexpectedstate criterion based on the determining that the package will bererouted, modifying the assignment of the workload to reassign theworkload from the first edge gateway device to the second edge gatewaydevice.
 20. The non-transitory machine-readable medium of claim 19,wherein the operations further comprise: based on the provisioning data,determining path data indicative of a path via which the device isexpected to travel, wherein the path comprises the first path; andselecting the first edge gateway device based on the path data.